{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5525bf60",
   "metadata": {},
   "source": [
    "# MobileNet V1 模型总结\n",
    "\n",
    "## 关键创新点\n",
    "\n",
    "### 1. 深度可分离卷积（Depthwise Separable Convolution）\n",
    "- 将标准卷积分解为两个独立操作：\n",
    "  - **Depthwise Convolution（逐通道卷积）**：对每个输入通道单独进行卷积操作。\n",
    "  - **Pointwise Convolution（逐点卷积）**：使用 `1x1` 卷积融合不同通道的信息。\n",
    "- 大幅减少了计算量和参数数量，适用于移动端和嵌入式设备。这一点和新出的MLP-Mixer的结构及其接近.\n",
    "\n",
    "### 2. 轻量化设计\n",
    "- 在保证准确率的前提下，通过减少冗余计算实现高效推理。\n",
    "- 相比传统卷积网络（如VGG、Inception等），模型大小显著减小。\n",
    "\n",
    "### 3. 宽度乘子（Width Multiplier）\n",
    "- 引入超参数 `α ∈ (0,1]` 控制输入输出通道数，进一步压缩模型。\n",
    "- 可以在精度与速度之间做权衡，提升部署灵活性。\n",
    "\n",
    "### 4. 分辨率乘子（Resolution Multiplier）\n",
    "- 控制输入图像的分辨率，作为另一个控制模型复杂度的参数。\n",
    "- 允许根据设备性能调整输入尺寸，从而影响整体计算量。\n",
    "\n",
    "### 5. 模块化结构\n",
    "- 整体网络由多个堆叠的深度可分离卷积模块构成，便于复用和扩展。\n",
    "\n",
    "---\n",
    "\n",
    "## 缺点与局限性\n",
    "\n",
    "### 1. 精度略低于标准模型\n",
    "- 在相同数据集下，相比 ResNet、Inception 等大型模型，在 Top-1 准确率上略有下降。\n",
    "\n",
    "### 2. 感受野受限\n",
    "- 使用较多的小卷积核（如 3x3）和深度卷积，可能限制了特征提取的感受野范围。\n",
    "\n",
    "### 3. 依赖手动设计\n",
    "- 网络结构是人工设计的，没有像后续版本（如 MobileNetV2、NASNet）那样利用神经网络架构搜索（NAS）来优化性能。\n",
    "\n",
    "### 4. 信息流动效率较低\n",
    "- 深度可分离卷积可能导致特征表达能力受限，特别是在高层语义任务中表现不如密集连接的网络。\n",
    "\n",
    "---\n",
    "\n",
    "## 总结\n",
    "\n",
    "MobileNet V1 是一个开创性的轻量级卷积神经网络，其核心思想 —— **深度可分离卷积**，为后续轻量化模型的发展奠定了基础。尽管它在精度和表达能力上有所妥协，但在移动设备和边缘计算场景中具有重要的应用价值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5d1e83",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70544e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d1e1748",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(10),\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7e8af81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 3228170\n",
      "Epoch: 1/100 Train Loss: 1.9463 Accuracy: 0.3403 Time: 10.84477  | Val Loss: 1.7106 Accuracy: 0.4645\n",
      "Epoch: 2/100 Train Loss: 1.6502 Accuracy: 0.5048 Time: 11.20975  | Val Loss: 1.5851 Accuracy: 0.5361\n",
      "Epoch: 3/100 Train Loss: 1.5138 Accuracy: 0.5675 Time: 12.50287  | Val Loss: 1.5415 Accuracy: 0.5725\n",
      "Epoch: 4/100 Train Loss: 1.3966 Accuracy: 0.6190 Time: 11.35471  | Val Loss: 1.3085 Accuracy: 0.6459\n",
      "Epoch: 5/100 Train Loss: 1.3249 Accuracy: 0.6478 Time: 10.26332  | Val Loss: 1.2718 Accuracy: 0.6859\n",
      "Epoch: 6/100 Train Loss: 1.2855 Accuracy: 0.6732 Time: 10.22753  | Val Loss: 1.2369 Accuracy: 0.7080\n",
      "Epoch: 7/100 Train Loss: 1.2476 Accuracy: 0.6963 Time: 11.17076  | Val Loss: 1.2019 Accuracy: 0.7111\n",
      "Epoch: 8/100 Train Loss: 1.1980 Accuracy: 0.7114 Time: 12.06234  | Val Loss: 1.1339 Accuracy: 0.7447\n",
      "Epoch: 9/100 Train Loss: 1.1789 Accuracy: 0.7207 Time: 11.74540  | Val Loss: 1.0687 Accuracy: 0.7715\n",
      "Epoch: 10/100 Train Loss: 1.1362 Accuracy: 0.7431 Time: 10.94621  | Val Loss: 1.1354 Accuracy: 0.7503\n",
      "Epoch: 11/100 Train Loss: 1.1162 Accuracy: 0.7456 Time: 10.12659  | Val Loss: 1.0966 Accuracy: 0.7664\n",
      "Epoch: 12/100 Train Loss: 1.0976 Accuracy: 0.7582 Time: 10.98347  | Val Loss: 1.0575 Accuracy: 0.7868\n",
      "Epoch: 13/100 Train Loss: 1.0621 Accuracy: 0.7732 Time: 11.30991  | Val Loss: 1.0391 Accuracy: 0.7911\n",
      "Epoch: 14/100 Train Loss: 1.0498 Accuracy: 0.7788 Time: 11.92183  | Val Loss: 1.0436 Accuracy: 0.7773\n",
      "Epoch: 15/100 Train Loss: 1.0283 Accuracy: 0.7853 Time: 11.39590  | Val Loss: 0.9532 Accuracy: 0.8232\n",
      "Epoch: 16/100 Train Loss: 1.0195 Accuracy: 0.7926 Time: 9.78950  | Val Loss: 1.0392 Accuracy: 0.7885\n",
      "Epoch: 17/100 Train Loss: 1.0050 Accuracy: 0.7997 Time: 10.18489  | Val Loss: 0.9987 Accuracy: 0.8074\n",
      "Epoch: 18/100 Train Loss: 0.9865 Accuracy: 0.8057 Time: 11.22964  | Val Loss: 0.9330 Accuracy: 0.8316\n",
      "Epoch: 19/100 Train Loss: 0.9729 Accuracy: 0.8103 Time: 12.14791  | Val Loss: 0.9538 Accuracy: 0.8163\n",
      "Epoch: 20/100 Train Loss: 0.9574 Accuracy: 0.8138 Time: 11.59121  | Val Loss: 0.9303 Accuracy: 0.8296\n",
      "Epoch: 21/100 Train Loss: 0.9374 Accuracy: 0.8267 Time: 10.75070  | Val Loss: 0.9458 Accuracy: 0.8227\n",
      "Epoch: 22/100 Train Loss: 0.9314 Accuracy: 0.8268 Time: 10.03685  | Val Loss: 0.9624 Accuracy: 0.8206\n",
      "Epoch: 23/100 Train Loss: 0.9205 Accuracy: 0.8322 Time: 10.83161  | Val Loss: 0.9075 Accuracy: 0.8408\n",
      "Epoch: 24/100 Train Loss: 0.9066 Accuracy: 0.8382 Time: 11.29506  | Val Loss: 0.9247 Accuracy: 0.8423\n",
      "Epoch: 25/100 Train Loss: 0.8998 Accuracy: 0.8406 Time: 12.26167  | Val Loss: 0.9039 Accuracy: 0.8451\n",
      "Epoch: 26/100 Train Loss: 0.8932 Accuracy: 0.8461 Time: 11.53452  | Val Loss: 0.8988 Accuracy: 0.8425\n",
      "Epoch: 27/100 Train Loss: 0.8736 Accuracy: 0.8576 Time: 10.45467  | Val Loss: 0.8595 Accuracy: 0.8611\n",
      "Epoch: 28/100 Train Loss: 0.8562 Accuracy: 0.8609 Time: 9.99727  | Val Loss: 0.9156 Accuracy: 0.8448\n",
      "Epoch: 29/100 Train Loss: 0.8536 Accuracy: 0.8605 Time: 11.28637  | Val Loss: 0.8882 Accuracy: 0.8499\n",
      "Epoch: 30/100 Train Loss: 0.8546 Accuracy: 0.8623 Time: 11.96208  | Val Loss: 0.8761 Accuracy: 0.8571\n",
      "Epoch: 31/100 Train Loss: 0.8382 Accuracy: 0.8678 Time: 11.73205  | Val Loss: 0.8599 Accuracy: 0.8604\n",
      "Epoch: 32/100 Train Loss: 0.8307 Accuracy: 0.8724 Time: 10.95037  | Val Loss: 0.8798 Accuracy: 0.8507\n",
      "Epoch: 33/100 Train Loss: 0.8249 Accuracy: 0.8756 Time: 10.18682  | Val Loss: 0.8646 Accuracy: 0.8599\n",
      "Epoch: 34/100 Train Loss: 0.8071 Accuracy: 0.8834 Time: 10.68036  | Val Loss: 0.8412 Accuracy: 0.8706\n",
      "Epoch: 35/100 Train Loss: 0.8018 Accuracy: 0.8873 Time: 11.44319  | Val Loss: 0.8551 Accuracy: 0.8652\n",
      "Epoch: 36/100 Train Loss: 0.7975 Accuracy: 0.8901 Time: 12.16401  | Val Loss: 0.8507 Accuracy: 0.8619\n",
      "Epoch: 37/100 Train Loss: 0.7896 Accuracy: 0.8910 Time: 11.25565  | Val Loss: 0.8391 Accuracy: 0.8685\n",
      "Epoch: 38/100 Train Loss: 0.7705 Accuracy: 0.9009 Time: 10.07988  | Val Loss: 0.8238 Accuracy: 0.8752\n",
      "Epoch: 39/100 Train Loss: 0.7791 Accuracy: 0.8978 Time: 9.98537  | Val Loss: 0.8528 Accuracy: 0.8680\n",
      "Epoch: 40/100 Train Loss: 0.7706 Accuracy: 0.8973 Time: 11.13880  | Val Loss: 0.8392 Accuracy: 0.8749\n",
      "Epoch: 41/100 Train Loss: 0.7635 Accuracy: 0.9050 Time: 12.29118  | Val Loss: 0.8432 Accuracy: 0.8662\n",
      "Epoch: 42/100 Train Loss: 0.7479 Accuracy: 0.9107 Time: 11.56360  | Val Loss: 0.8419 Accuracy: 0.8655\n",
      "Epoch: 43/100 Train Loss: 0.7539 Accuracy: 0.9059 Time: 10.61067  | Val Loss: 0.8374 Accuracy: 0.8655\n",
      "Epoch: 44/100 Train Loss: 0.7364 Accuracy: 0.9145 Time: 10.11436  | Val Loss: 0.8044 Accuracy: 0.8813\n",
      "Epoch: 45/100 Train Loss: 0.7220 Accuracy: 0.9203 Time: 11.05045  | Val Loss: 0.8176 Accuracy: 0.8729\n",
      "Epoch: 46/100 Train Loss: 0.7271 Accuracy: 0.9171 Time: 11.73259  | Val Loss: 0.8179 Accuracy: 0.8764\n",
      "Epoch: 47/100 Train Loss: 0.7201 Accuracy: 0.9213 Time: 12.40211  | Val Loss: 0.8037 Accuracy: 0.8838\n",
      "Epoch: 48/100 Train Loss: 0.7126 Accuracy: 0.9236 Time: 11.47309  | Val Loss: 0.8023 Accuracy: 0.8846\n",
      "Epoch: 49/100 Train Loss: 0.7107 Accuracy: 0.9255 Time: 9.97580  | Val Loss: 0.8098 Accuracy: 0.8808\n",
      "Epoch: 50/100 Train Loss: 0.6991 Accuracy: 0.9309 Time: 10.28719  | Val Loss: 0.8175 Accuracy: 0.8803\n",
      "Epoch: 51/100 Train Loss: 0.7020 Accuracy: 0.9289 Time: 11.31629  | Val Loss: 0.8238 Accuracy: 0.8739\n",
      "Epoch: 52/100 Train Loss: 0.6945 Accuracy: 0.9346 Time: 12.28165  | Val Loss: 0.7905 Accuracy: 0.8869\n",
      "Epoch: 53/100 Train Loss: 0.6861 Accuracy: 0.9372 Time: 11.57586  | Val Loss: 0.7855 Accuracy: 0.8920\n",
      "Epoch: 54/100 Train Loss: 0.6881 Accuracy: 0.9324 Time: 10.11854  | Val Loss: 0.7957 Accuracy: 0.8876\n",
      "Epoch: 55/100 Train Loss: 0.6834 Accuracy: 0.9360 Time: 10.17540  | Val Loss: 0.7845 Accuracy: 0.8925\n",
      "Epoch: 56/100 Train Loss: 0.6702 Accuracy: 0.9453 Time: 11.41784  | Val Loss: 0.8009 Accuracy: 0.8854\n",
      "Epoch: 57/100 Train Loss: 0.6677 Accuracy: 0.9439 Time: 12.36276  | Val Loss: 0.7995 Accuracy: 0.8851\n",
      "Epoch: 58/100 Train Loss: 0.6636 Accuracy: 0.9460 Time: 11.64152  | Val Loss: 0.7815 Accuracy: 0.8912\n",
      "Epoch: 59/100 Train Loss: 0.6683 Accuracy: 0.9446 Time: 10.55734  | Val Loss: 0.7981 Accuracy: 0.8884\n",
      "Epoch: 60/100 Train Loss: 0.6582 Accuracy: 0.9493 Time: 10.28439  | Val Loss: 0.7940 Accuracy: 0.8876\n",
      "Epoch: 61/100 Train Loss: 0.6554 Accuracy: 0.9489 Time: 10.93102  | Val Loss: 0.7712 Accuracy: 0.8953\n",
      "Epoch: 62/100 Train Loss: 0.6483 Accuracy: 0.9519 Time: 12.10558  | Val Loss: 0.7720 Accuracy: 0.8978\n",
      "Epoch: 63/100 Train Loss: 0.6462 Accuracy: 0.9547 Time: 11.72612  | Val Loss: 0.7730 Accuracy: 0.8940\n",
      "Epoch: 64/100 Train Loss: 0.6491 Accuracy: 0.9522 Time: 10.84718  | Val Loss: 0.7740 Accuracy: 0.8976\n",
      "Epoch: 65/100 Train Loss: 0.6436 Accuracy: 0.9543 Time: 9.85537  | Val Loss: 0.7782 Accuracy: 0.8925\n",
      "Epoch: 66/100 Train Loss: 0.6378 Accuracy: 0.9588 Time: 10.35691  | Val Loss: 0.7670 Accuracy: 0.8994\n",
      "Epoch: 67/100 Train Loss: 0.6311 Accuracy: 0.9619 Time: 11.11492  | Val Loss: 0.7752 Accuracy: 0.8945\n",
      "Epoch: 68/100 Train Loss: 0.6272 Accuracy: 0.9615 Time: 12.08783  | Val Loss: 0.7660 Accuracy: 0.8981\n",
      "Epoch: 69/100 Train Loss: 0.6258 Accuracy: 0.9630 Time: 11.12189  | Val Loss: 0.7717 Accuracy: 0.8953\n",
      "Epoch: 70/100 Train Loss: 0.6239 Accuracy: 0.9640 Time: 9.96852  | Val Loss: 0.7571 Accuracy: 0.9004\n",
      "Epoch: 71/100 Train Loss: 0.6254 Accuracy: 0.9607 Time: 9.82071  | Val Loss: 0.7647 Accuracy: 0.8994\n",
      "Epoch: 72/100 Train Loss: 0.6212 Accuracy: 0.9639 Time: 10.98966  | Val Loss: 0.7725 Accuracy: 0.8986\n",
      "Epoch: 73/100 Train Loss: 0.6183 Accuracy: 0.9647 Time: 11.81833  | Val Loss: 0.7613 Accuracy: 0.8994\n",
      "Epoch: 74/100 Train Loss: 0.6121 Accuracy: 0.9673 Time: 11.30618  | Val Loss: 0.7700 Accuracy: 0.8968\n",
      "Epoch: 75/100 Train Loss: 0.6176 Accuracy: 0.9659 Time: 10.61926  | Val Loss: 0.7650 Accuracy: 0.9001\n",
      "Epoch: 76/100 Train Loss: 0.6075 Accuracy: 0.9698 Time: 9.85692  | Val Loss: 0.7693 Accuracy: 0.8971\n",
      "Epoch: 77/100 Train Loss: 0.6084 Accuracy: 0.9673 Time: 10.45815  | Val Loss: 0.7615 Accuracy: 0.8978\n",
      "Epoch: 78/100 Train Loss: 0.6132 Accuracy: 0.9658 Time: 11.02506  | Val Loss: 0.7607 Accuracy: 0.9014\n",
      "Epoch: 79/100 Train Loss: 0.6084 Accuracy: 0.9691 Time: 12.03882  | Val Loss: 0.7648 Accuracy: 0.8983\n",
      "Epoch: 80/100 Train Loss: 0.6020 Accuracy: 0.9721 Time: 11.31021  | Val Loss: 0.7626 Accuracy: 0.8999\n",
      "Epoch: 81/100 Train Loss: 0.6024 Accuracy: 0.9721 Time: 9.82942  | Val Loss: 0.7584 Accuracy: 0.9034\n",
      "Epoch: 82/100 Train Loss: 0.6010 Accuracy: 0.9721 Time: 9.88482  | Val Loss: 0.7602 Accuracy: 0.8981\n",
      "Epoch: 83/100 Train Loss: 0.6012 Accuracy: 0.9721 Time: 10.97046  | Val Loss: 0.7617 Accuracy: 0.9001\n",
      "Epoch: 84/100 Train Loss: 0.5970 Accuracy: 0.9719 Time: 11.95373  | Val Loss: 0.7595 Accuracy: 0.9014\n",
      "Epoch: 85/100 Train Loss: 0.5947 Accuracy: 0.9742 Time: 11.20737  | Val Loss: 0.7550 Accuracy: 0.9022\n",
      "Epoch: 86/100 Train Loss: 0.5958 Accuracy: 0.9733 Time: 10.36012  | Val Loss: 0.7517 Accuracy: 0.9057\n",
      "Epoch: 87/100 Train Loss: 0.5968 Accuracy: 0.9722 Time: 9.65063  | Val Loss: 0.7559 Accuracy: 0.9050\n",
      "Epoch: 88/100 Train Loss: 0.5905 Accuracy: 0.9757 Time: 10.58842  | Val Loss: 0.7562 Accuracy: 0.9045\n",
      "Epoch: 89/100 Train Loss: 0.5894 Accuracy: 0.9758 Time: 11.50602  | Val Loss: 0.7557 Accuracy: 0.9050\n",
      "Epoch: 90/100 Train Loss: 0.5914 Accuracy: 0.9763 Time: 11.50456  | Val Loss: 0.7547 Accuracy: 0.9057\n",
      "Epoch: 91/100 Train Loss: 0.5893 Accuracy: 0.9755 Time: 10.77473  | Val Loss: 0.7526 Accuracy: 0.9060\n",
      "Epoch: 92/100 Train Loss: 0.5911 Accuracy: 0.9748 Time: 9.78139  | Val Loss: 0.7519 Accuracy: 0.9057\n",
      "Epoch: 93/100 Train Loss: 0.5897 Accuracy: 0.9772 Time: 10.24108  | Val Loss: 0.7540 Accuracy: 0.9045\n",
      "Epoch: 94/100 Train Loss: 0.5886 Accuracy: 0.9777 Time: 10.86628  | Val Loss: 0.7543 Accuracy: 0.9027\n",
      "Epoch: 95/100 Train Loss: 0.5879 Accuracy: 0.9741 Time: 11.81622  | Val Loss: 0.7485 Accuracy: 0.9080\n",
      "Epoch: 96/100 Train Loss: 0.5850 Accuracy: 0.9784 Time: 11.26774  | Val Loss: 0.7553 Accuracy: 0.9029\n",
      "Epoch: 97/100 Train Loss: 0.5884 Accuracy: 0.9766 Time: 9.66033  | Val Loss: 0.7518 Accuracy: 0.9034\n",
      "Epoch: 98/100 Train Loss: 0.5881 Accuracy: 0.9759 Time: 9.60732  | Val Loss: 0.7555 Accuracy: 0.9042\n",
      "Epoch: 99/100 Train Loss: 0.5903 Accuracy: 0.9759 Time: 10.73003  | Val Loss: 0.7541 Accuracy: 0.9032\n",
      "Epoch: 100/100 Train Loss: 0.5851 Accuracy: 0.9776 Time: 11.66982  | Val Loss: 0.7518 Accuracy: 0.9034\n",
      "#Parameter: 3228170 Accuracy: 0.9034394904458599\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.mobilenet_v1 import MobileNetV1\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-5,\n",
    "    max_epochs=100,\n",
    ") -> tuple[MobileNetV1, dict[str, list[float]]]:\n",
    "    net = MobileNetV1(num_classes=10, width_multiplier=width_multiplier).to(DEVICE)\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "\n",
    "net, width_multiplier_1 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=1,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37f41a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 1832458\n",
      "Epoch: 1/100 Train Loss: 1.9838 Accuracy: 0.3209 Time: 8.84561  | Val Loss: 1.7374 Accuracy: 0.4555\n",
      "Epoch: 2/100 Train Loss: 1.7226 Accuracy: 0.4672 Time: 9.60424  | Val Loss: 1.7083 Accuracy: 0.4958\n",
      "Epoch: 3/100 Train Loss: 1.5929 Accuracy: 0.5289 Time: 8.51767  | Val Loss: 1.5186 Accuracy: 0.5656\n",
      "Epoch: 4/100 Train Loss: 1.4970 Accuracy: 0.5756 Time: 8.69518  | Val Loss: 1.4907 Accuracy: 0.5985\n",
      "Epoch: 5/100 Train Loss: 1.4205 Accuracy: 0.6132 Time: 9.62786  | Val Loss: 1.3374 Accuracy: 0.6525\n",
      "Epoch: 6/100 Train Loss: 1.3724 Accuracy: 0.6317 Time: 9.07552  | Val Loss: 1.2628 Accuracy: 0.6800\n",
      "Epoch: 7/100 Train Loss: 1.3105 Accuracy: 0.6610 Time: 7.98369  | Val Loss: 1.1988 Accuracy: 0.7096\n",
      "Epoch: 8/100 Train Loss: 1.2688 Accuracy: 0.6786 Time: 9.35858  | Val Loss: 1.2670 Accuracy: 0.6780\n",
      "Epoch: 9/100 Train Loss: 1.2183 Accuracy: 0.7002 Time: 9.14687  | Val Loss: 1.1775 Accuracy: 0.7152\n",
      "Epoch: 10/100 Train Loss: 1.2083 Accuracy: 0.7075 Time: 7.90328  | Val Loss: 1.1302 Accuracy: 0.7424\n",
      "Epoch: 11/100 Train Loss: 1.1768 Accuracy: 0.7229 Time: 8.86263  | Val Loss: 1.1389 Accuracy: 0.7437\n",
      "Epoch: 12/100 Train Loss: 1.1416 Accuracy: 0.7377 Time: 9.78143  | Val Loss: 1.0596 Accuracy: 0.7720\n",
      "Epoch: 13/100 Train Loss: 1.1190 Accuracy: 0.7472 Time: 8.93829  | Val Loss: 1.1311 Accuracy: 0.7475\n",
      "Epoch: 14/100 Train Loss: 1.0935 Accuracy: 0.7611 Time: 8.42629  | Val Loss: 1.0692 Accuracy: 0.7671\n",
      "Epoch: 15/100 Train Loss: 1.0770 Accuracy: 0.7658 Time: 9.55531  | Val Loss: 1.1073 Accuracy: 0.7575\n",
      "Epoch: 16/100 Train Loss: 1.0637 Accuracy: 0.7728 Time: 9.19334  | Val Loss: 1.0161 Accuracy: 0.7931\n",
      "Epoch: 17/100 Train Loss: 1.0410 Accuracy: 0.7795 Time: 7.89553  | Val Loss: 1.0245 Accuracy: 0.7921\n",
      "Epoch: 18/100 Train Loss: 1.0335 Accuracy: 0.7857 Time: 8.89921  | Val Loss: 0.9867 Accuracy: 0.8104\n",
      "Epoch: 19/100 Train Loss: 1.0043 Accuracy: 0.7934 Time: 9.72785  | Val Loss: 0.9960 Accuracy: 0.8087\n",
      "Epoch: 20/100 Train Loss: 0.9979 Accuracy: 0.8017 Time: 8.58898  | Val Loss: 0.9498 Accuracy: 0.8283\n",
      "Epoch: 21/100 Train Loss: 0.9830 Accuracy: 0.8048 Time: 8.65423  | Val Loss: 0.9338 Accuracy: 0.8257\n",
      "Epoch: 22/100 Train Loss: 0.9575 Accuracy: 0.8187 Time: 6.47281  | Val Loss: 0.9739 Accuracy: 0.8201\n",
      "Epoch: 23/100 Train Loss: 0.9662 Accuracy: 0.8142 Time: 6.60839  | Val Loss: 0.9237 Accuracy: 0.8341\n",
      "Epoch: 24/100 Train Loss: 0.9558 Accuracy: 0.8189 Time: 6.71492  | Val Loss: 0.9237 Accuracy: 0.8334\n",
      "Epoch: 25/100 Train Loss: 0.9392 Accuracy: 0.8289 Time: 6.63409  | Val Loss: 0.9661 Accuracy: 0.8122\n",
      "Epoch: 26/100 Train Loss: 0.9309 Accuracy: 0.8298 Time: 6.69758  | Val Loss: 0.9452 Accuracy: 0.8260\n",
      "Epoch: 27/100 Train Loss: 0.9243 Accuracy: 0.8331 Time: 9.48098  | Val Loss: 0.9540 Accuracy: 0.8283\n",
      "Epoch: 28/100 Train Loss: 0.9086 Accuracy: 0.8410 Time: 8.95873  | Val Loss: 0.9261 Accuracy: 0.8336\n",
      "Epoch: 29/100 Train Loss: 0.8960 Accuracy: 0.8464 Time: 7.90832  | Val Loss: 0.9120 Accuracy: 0.8405\n",
      "Epoch: 30/100 Train Loss: 0.8871 Accuracy: 0.8474 Time: 8.08313  | Val Loss: 0.8865 Accuracy: 0.8548\n",
      "Epoch: 31/100 Train Loss: 0.8727 Accuracy: 0.8512 Time: 8.64007  | Val Loss: 0.8879 Accuracy: 0.8507\n",
      "Epoch: 32/100 Train Loss: 0.8731 Accuracy: 0.8530 Time: 9.66222  | Val Loss: 0.8686 Accuracy: 0.8578\n",
      "Epoch: 33/100 Train Loss: 0.8560 Accuracy: 0.8602 Time: 9.31399  | Val Loss: 0.8953 Accuracy: 0.8456\n",
      "Epoch: 34/100 Train Loss: 0.8435 Accuracy: 0.8700 Time: 8.76568  | Val Loss: 0.8974 Accuracy: 0.8474\n",
      "Epoch: 35/100 Train Loss: 0.8437 Accuracy: 0.8698 Time: 7.94224  | Val Loss: 0.8617 Accuracy: 0.8594\n",
      "Epoch: 36/100 Train Loss: 0.8413 Accuracy: 0.8665 Time: 8.83535  | Val Loss: 0.8523 Accuracy: 0.8673\n",
      "Epoch: 37/100 Train Loss: 0.8338 Accuracy: 0.8725 Time: 9.25490  | Val Loss: 0.8653 Accuracy: 0.8594\n",
      "Epoch: 38/100 Train Loss: 0.8127 Accuracy: 0.8812 Time: 9.87751  | Val Loss: 0.8472 Accuracy: 0.8693\n",
      "Epoch: 39/100 Train Loss: 0.8196 Accuracy: 0.8797 Time: 9.20839  | Val Loss: 0.8532 Accuracy: 0.8673\n",
      "Epoch: 40/100 Train Loss: 0.8046 Accuracy: 0.8856 Time: 8.53931  | Val Loss: 0.8913 Accuracy: 0.8390\n",
      "Epoch: 41/100 Train Loss: 0.7964 Accuracy: 0.8905 Time: 8.03263  | Val Loss: 0.8443 Accuracy: 0.8634\n",
      "Epoch: 42/100 Train Loss: 0.7869 Accuracy: 0.8924 Time: 8.83625  | Val Loss: 0.8737 Accuracy: 0.8614\n",
      "Epoch: 43/100 Train Loss: 0.7802 Accuracy: 0.8995 Time: 9.46629  | Val Loss: 0.8386 Accuracy: 0.8698\n",
      "Epoch: 44/100 Train Loss: 0.7773 Accuracy: 0.9018 Time: 9.87678  | Val Loss: 0.8503 Accuracy: 0.8678\n",
      "Epoch: 45/100 Train Loss: 0.7765 Accuracy: 0.8976 Time: 9.31906  | Val Loss: 0.8395 Accuracy: 0.8652\n",
      "Epoch: 46/100 Train Loss: 0.7708 Accuracy: 0.8996 Time: 8.15390  | Val Loss: 0.8539 Accuracy: 0.8678\n",
      "Epoch: 47/100 Train Loss: 0.7577 Accuracy: 0.9123 Time: 8.17328  | Val Loss: 0.8363 Accuracy: 0.8680\n",
      "Epoch: 48/100 Train Loss: 0.7524 Accuracy: 0.9088 Time: 8.91286  | Val Loss: 0.8528 Accuracy: 0.8637\n",
      "Epoch: 49/100 Train Loss: 0.7539 Accuracy: 0.9111 Time: 9.82594  | Val Loss: 0.8305 Accuracy: 0.8729\n",
      "Epoch: 50/100 Train Loss: 0.7443 Accuracy: 0.9120 Time: 9.19711  | Val Loss: 0.8391 Accuracy: 0.8769\n",
      "Epoch: 51/100 Train Loss: 0.7427 Accuracy: 0.9146 Time: 8.87080  | Val Loss: 0.8306 Accuracy: 0.8772\n",
      "Epoch: 52/100 Train Loss: 0.7343 Accuracy: 0.9177 Time: 7.85190  | Val Loss: 0.8241 Accuracy: 0.8764\n",
      "Epoch: 53/100 Train Loss: 0.7240 Accuracy: 0.9213 Time: 8.65930  | Val Loss: 0.8140 Accuracy: 0.8792\n",
      "Epoch: 54/100 Train Loss: 0.7227 Accuracy: 0.9225 Time: 8.99101  | Val Loss: 0.8193 Accuracy: 0.8769\n",
      "Epoch: 55/100 Train Loss: 0.7219 Accuracy: 0.9238 Time: 9.77596  | Val Loss: 0.8194 Accuracy: 0.8828\n",
      "Epoch: 56/100 Train Loss: 0.7080 Accuracy: 0.9327 Time: 9.12508  | Val Loss: 0.8210 Accuracy: 0.8800\n",
      "Epoch: 57/100 Train Loss: 0.7043 Accuracy: 0.9308 Time: 8.48299  | Val Loss: 0.8276 Accuracy: 0.8746\n",
      "Epoch: 58/100 Train Loss: 0.7029 Accuracy: 0.9317 Time: 8.08677  | Val Loss: 0.8134 Accuracy: 0.8843\n",
      "Epoch: 59/100 Train Loss: 0.7031 Accuracy: 0.9303 Time: 8.91084  | Val Loss: 0.8145 Accuracy: 0.8803\n",
      "Epoch: 60/100 Train Loss: 0.7021 Accuracy: 0.9323 Time: 9.54534  | Val Loss: 0.8066 Accuracy: 0.8828\n",
      "Epoch: 61/100 Train Loss: 0.6931 Accuracy: 0.9367 Time: 9.37767  | Val Loss: 0.7989 Accuracy: 0.8869\n",
      "Epoch: 62/100 Train Loss: 0.6799 Accuracy: 0.9437 Time: 8.97865  | Val Loss: 0.8036 Accuracy: 0.8874\n",
      "Epoch: 63/100 Train Loss: 0.6848 Accuracy: 0.9392 Time: 7.72303  | Val Loss: 0.7950 Accuracy: 0.8866\n",
      "Epoch: 64/100 Train Loss: 0.6800 Accuracy: 0.9396 Time: 8.21486  | Val Loss: 0.8067 Accuracy: 0.8864\n",
      "Epoch: 65/100 Train Loss: 0.6751 Accuracy: 0.9453 Time: 8.85144  | Val Loss: 0.7977 Accuracy: 0.8871\n",
      "Epoch: 66/100 Train Loss: 0.6723 Accuracy: 0.9459 Time: 9.50905  | Val Loss: 0.7950 Accuracy: 0.8902\n",
      "Epoch: 67/100 Train Loss: 0.6716 Accuracy: 0.9454 Time: 8.98051  | Val Loss: 0.8012 Accuracy: 0.8866\n",
      "Epoch: 68/100 Train Loss: 0.6695 Accuracy: 0.9449 Time: 8.20360  | Val Loss: 0.7877 Accuracy: 0.8917\n",
      "Epoch: 69/100 Train Loss: 0.6648 Accuracy: 0.9487 Time: 7.85282  | Val Loss: 0.7874 Accuracy: 0.8902\n",
      "Epoch: 70/100 Train Loss: 0.6638 Accuracy: 0.9474 Time: 8.64915  | Val Loss: 0.7973 Accuracy: 0.8859\n",
      "Epoch: 71/100 Train Loss: 0.6561 Accuracy: 0.9495 Time: 9.49492  | Val Loss: 0.7856 Accuracy: 0.8917\n",
      "Epoch: 72/100 Train Loss: 0.6540 Accuracy: 0.9525 Time: 9.00991  | Val Loss: 0.7825 Accuracy: 0.8910\n",
      "Epoch: 73/100 Train Loss: 0.6536 Accuracy: 0.9502 Time: 8.78758  | Val Loss: 0.7894 Accuracy: 0.8882\n",
      "Epoch: 74/100 Train Loss: 0.6513 Accuracy: 0.9537 Time: 7.72673  | Val Loss: 0.7852 Accuracy: 0.8904\n",
      "Epoch: 75/100 Train Loss: 0.6467 Accuracy: 0.9556 Time: 8.46094  | Val Loss: 0.7811 Accuracy: 0.8932\n",
      "Epoch: 76/100 Train Loss: 0.6383 Accuracy: 0.9610 Time: 8.89831  | Val Loss: 0.7809 Accuracy: 0.8927\n",
      "Epoch: 77/100 Train Loss: 0.6373 Accuracy: 0.9603 Time: 9.65052  | Val Loss: 0.7876 Accuracy: 0.8917\n",
      "Epoch: 78/100 Train Loss: 0.6442 Accuracy: 0.9572 Time: 9.02142  | Val Loss: 0.7833 Accuracy: 0.8927\n",
      "Epoch: 79/100 Train Loss: 0.6391 Accuracy: 0.9583 Time: 8.03571  | Val Loss: 0.7818 Accuracy: 0.8935\n",
      "Epoch: 80/100 Train Loss: 0.6413 Accuracy: 0.9577 Time: 7.85072  | Val Loss: 0.7822 Accuracy: 0.8932\n",
      "Epoch: 81/100 Train Loss: 0.6268 Accuracy: 0.9651 Time: 8.73048  | Val Loss: 0.7843 Accuracy: 0.8948\n",
      "Epoch: 82/100 Train Loss: 0.6359 Accuracy: 0.9599 Time: 9.51670  | Val Loss: 0.7777 Accuracy: 0.8927\n",
      "Epoch: 83/100 Train Loss: 0.6263 Accuracy: 0.9666 Time: 8.96593  | Val Loss: 0.7759 Accuracy: 0.8958\n",
      "Epoch: 84/100 Train Loss: 0.6260 Accuracy: 0.9651 Time: 8.73885  | Val Loss: 0.7788 Accuracy: 0.8955\n",
      "Epoch: 85/100 Train Loss: 0.6241 Accuracy: 0.9656 Time: 7.63520  | Val Loss: 0.7771 Accuracy: 0.8950\n",
      "Epoch: 86/100 Train Loss: 0.6286 Accuracy: 0.9648 Time: 8.53710  | Val Loss: 0.7735 Accuracy: 0.8953\n",
      "Epoch: 87/100 Train Loss: 0.6253 Accuracy: 0.9655 Time: 8.97207  | Val Loss: 0.7757 Accuracy: 0.8953\n",
      "Epoch: 88/100 Train Loss: 0.6309 Accuracy: 0.9611 Time: 9.70781  | Val Loss: 0.7781 Accuracy: 0.8948\n",
      "Epoch: 89/100 Train Loss: 0.6199 Accuracy: 0.9680 Time: 9.40281  | Val Loss: 0.7716 Accuracy: 0.8940\n",
      "Epoch: 90/100 Train Loss: 0.6264 Accuracy: 0.9636 Time: 8.22924  | Val Loss: 0.7742 Accuracy: 0.8961\n",
      "Epoch: 91/100 Train Loss: 0.6211 Accuracy: 0.9680 Time: 8.18577  | Val Loss: 0.7693 Accuracy: 0.8973\n",
      "Epoch: 92/100 Train Loss: 0.6223 Accuracy: 0.9691 Time: 8.87689  | Val Loss: 0.7692 Accuracy: 0.8971\n",
      "Epoch: 93/100 Train Loss: 0.6226 Accuracy: 0.9654 Time: 9.47474  | Val Loss: 0.7707 Accuracy: 0.8953\n",
      "Epoch: 94/100 Train Loss: 0.6199 Accuracy: 0.9688 Time: 9.46305  | Val Loss: 0.7707 Accuracy: 0.8940\n",
      "Epoch: 95/100 Train Loss: 0.6310 Accuracy: 0.9608 Time: 9.10556  | Val Loss: 0.7768 Accuracy: 0.8917\n",
      "Epoch: 96/100 Train Loss: 0.6234 Accuracy: 0.9664 Time: 7.88871  | Val Loss: 0.7739 Accuracy: 0.8940\n",
      "Epoch: 97/100 Train Loss: 0.6221 Accuracy: 0.9664 Time: 8.48213  | Val Loss: 0.7713 Accuracy: 0.8943\n",
      "Epoch: 98/100 Train Loss: 0.6184 Accuracy: 0.9682 Time: 8.83777  | Val Loss: 0.7731 Accuracy: 0.8943\n",
      "Epoch: 99/100 Train Loss: 0.6170 Accuracy: 0.9685 Time: 9.88945  | Val Loss: 0.7703 Accuracy: 0.8938\n",
      "Epoch: 100/100 Train Loss: 0.6206 Accuracy: 0.9668 Time: 9.46379  | Val Loss: 0.7708 Accuracy: 0.8961\n",
      "#Parameter: 1832458 Accuracy: 0.8960509554140127\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_75 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.75,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f324653c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 829194\n",
      "Epoch: 1/100 Train Loss: 1.9936 Accuracy: 0.3139 Time: 6.86565  | Val Loss: 2.3369 Accuracy: 0.2978\n",
      "Epoch: 2/100 Train Loss: 1.7558 Accuracy: 0.4430 Time: 7.59550  | Val Loss: 1.6754 Accuracy: 0.5078\n",
      "Epoch: 3/100 Train Loss: 1.6337 Accuracy: 0.5026 Time: 7.09340  | Val Loss: 1.5563 Accuracy: 0.5424\n",
      "Epoch: 4/100 Train Loss: 1.5453 Accuracy: 0.5504 Time: 6.89551  | Val Loss: 1.5335 Accuracy: 0.5648\n",
      "Epoch: 5/100 Train Loss: 1.4806 Accuracy: 0.5782 Time: 7.34388  | Val Loss: 1.3876 Accuracy: 0.6280\n",
      "Epoch: 6/100 Train Loss: 1.4097 Accuracy: 0.6202 Time: 7.13816  | Val Loss: 1.3523 Accuracy: 0.6423\n",
      "Epoch: 7/100 Train Loss: 1.3729 Accuracy: 0.6254 Time: 6.96955  | Val Loss: 1.2841 Accuracy: 0.6869\n",
      "Epoch: 8/100 Train Loss: 1.3036 Accuracy: 0.6643 Time: 7.31129  | Val Loss: 1.2063 Accuracy: 0.7060\n",
      "Epoch: 9/100 Train Loss: 1.2708 Accuracy: 0.6741 Time: 7.12056  | Val Loss: 1.1858 Accuracy: 0.7182\n",
      "Epoch: 10/100 Train Loss: 1.2347 Accuracy: 0.6943 Time: 6.54942  | Val Loss: 1.1461 Accuracy: 0.7325\n",
      "Epoch: 11/100 Train Loss: 1.2243 Accuracy: 0.7029 Time: 7.25630  | Val Loss: 1.1307 Accuracy: 0.7399\n",
      "Epoch: 12/100 Train Loss: 1.1896 Accuracy: 0.7177 Time: 7.46404  | Val Loss: 1.1097 Accuracy: 0.7534\n",
      "Epoch: 13/100 Train Loss: 1.1802 Accuracy: 0.7191 Time: 6.48624  | Val Loss: 1.1125 Accuracy: 0.7503\n",
      "Epoch: 14/100 Train Loss: 1.1522 Accuracy: 0.7363 Time: 6.79023  | Val Loss: 1.0912 Accuracy: 0.7641\n",
      "Epoch: 15/100 Train Loss: 1.1274 Accuracy: 0.7435 Time: 7.59658  | Val Loss: 1.0816 Accuracy: 0.7666\n",
      "Epoch: 16/100 Train Loss: 1.1023 Accuracy: 0.7527 Time: 7.01627  | Val Loss: 1.0357 Accuracy: 0.7845\n",
      "Epoch: 17/100 Train Loss: 1.1076 Accuracy: 0.7493 Time: 6.73992  | Val Loss: 1.0250 Accuracy: 0.7926\n",
      "Epoch: 18/100 Train Loss: 1.0651 Accuracy: 0.7695 Time: 7.61143  | Val Loss: 1.0373 Accuracy: 0.7870\n",
      "Epoch: 19/100 Train Loss: 1.0638 Accuracy: 0.7716 Time: 7.00715  | Val Loss: 1.0247 Accuracy: 0.7946\n",
      "Epoch: 20/100 Train Loss: 1.0529 Accuracy: 0.7761 Time: 6.81612  | Val Loss: 0.9980 Accuracy: 0.7985\n",
      "Epoch: 21/100 Train Loss: 1.0307 Accuracy: 0.7846 Time: 7.45279  | Val Loss: 0.9679 Accuracy: 0.8181\n",
      "Epoch: 22/100 Train Loss: 1.0358 Accuracy: 0.7858 Time: 7.26686  | Val Loss: 0.9904 Accuracy: 0.8107\n",
      "Epoch: 23/100 Train Loss: 1.0062 Accuracy: 0.7935 Time: 6.66276  | Val Loss: 0.9856 Accuracy: 0.8097\n",
      "Epoch: 24/100 Train Loss: 1.0073 Accuracy: 0.7973 Time: 7.47915  | Val Loss: 0.9678 Accuracy: 0.8140\n",
      "Epoch: 25/100 Train Loss: 0.9817 Accuracy: 0.8079 Time: 7.11833  | Val Loss: 1.0319 Accuracy: 0.7906\n",
      "Epoch: 26/100 Train Loss: 0.9831 Accuracy: 0.8086 Time: 6.91599  | Val Loss: 0.9638 Accuracy: 0.8171\n",
      "Epoch: 27/100 Train Loss: 0.9686 Accuracy: 0.8118 Time: 7.51775  | Val Loss: 0.9337 Accuracy: 0.8341\n",
      "Epoch: 28/100 Train Loss: 0.9602 Accuracy: 0.8166 Time: 7.41410  | Val Loss: 0.9430 Accuracy: 0.8290\n",
      "Epoch: 29/100 Train Loss: 0.9554 Accuracy: 0.8191 Time: 6.79882  | Val Loss: 0.9507 Accuracy: 0.8222\n",
      "Epoch: 30/100 Train Loss: 0.9356 Accuracy: 0.8240 Time: 7.49036  | Val Loss: 0.9269 Accuracy: 0.8324\n",
      "Epoch: 31/100 Train Loss: 0.9364 Accuracy: 0.8279 Time: 7.23212  | Val Loss: 0.9150 Accuracy: 0.8397\n",
      "Epoch: 32/100 Train Loss: 0.9153 Accuracy: 0.8363 Time: 6.27846  | Val Loss: 0.9230 Accuracy: 0.8344\n",
      "Epoch: 33/100 Train Loss: 0.9175 Accuracy: 0.8359 Time: 6.90374  | Val Loss: 0.9155 Accuracy: 0.8397\n",
      "Epoch: 34/100 Train Loss: 0.9101 Accuracy: 0.8393 Time: 7.67879  | Val Loss: 0.9138 Accuracy: 0.8367\n",
      "Epoch: 35/100 Train Loss: 0.9106 Accuracy: 0.8393 Time: 6.61082  | Val Loss: 0.8978 Accuracy: 0.8469\n",
      "Epoch: 36/100 Train Loss: 0.8886 Accuracy: 0.8467 Time: 7.07839  | Val Loss: 0.8973 Accuracy: 0.8403\n",
      "Epoch: 37/100 Train Loss: 0.8942 Accuracy: 0.8452 Time: 7.54059  | Val Loss: 0.8938 Accuracy: 0.8464\n",
      "Epoch: 38/100 Train Loss: 0.8721 Accuracy: 0.8562 Time: 6.95844  | Val Loss: 0.8854 Accuracy: 0.8499\n",
      "Epoch: 39/100 Train Loss: 0.8768 Accuracy: 0.8548 Time: 6.91473  | Val Loss: 0.8821 Accuracy: 0.8476\n",
      "Epoch: 40/100 Train Loss: 0.8687 Accuracy: 0.8591 Time: 7.56108  | Val Loss: 0.8853 Accuracy: 0.8555\n",
      "Epoch: 41/100 Train Loss: 0.8578 Accuracy: 0.8607 Time: 7.05781  | Val Loss: 0.9081 Accuracy: 0.8385\n",
      "Epoch: 42/100 Train Loss: 0.8560 Accuracy: 0.8620 Time: 7.07597  | Val Loss: 0.9161 Accuracy: 0.8331\n",
      "Epoch: 43/100 Train Loss: 0.8473 Accuracy: 0.8674 Time: 7.70894  | Val Loss: 0.9020 Accuracy: 0.8448\n",
      "Epoch: 44/100 Train Loss: 0.8466 Accuracy: 0.8675 Time: 7.17464  | Val Loss: 0.8798 Accuracy: 0.8581\n",
      "Epoch: 45/100 Train Loss: 0.8402 Accuracy: 0.8704 Time: 6.88735  | Val Loss: 0.8686 Accuracy: 0.8571\n",
      "Epoch: 46/100 Train Loss: 0.8228 Accuracy: 0.8769 Time: 7.56871  | Val Loss: 0.8978 Accuracy: 0.8471\n",
      "Epoch: 47/100 Train Loss: 0.8228 Accuracy: 0.8738 Time: 7.20829  | Val Loss: 0.8684 Accuracy: 0.8599\n",
      "Epoch: 48/100 Train Loss: 0.8120 Accuracy: 0.8848 Time: 6.82906  | Val Loss: 0.8583 Accuracy: 0.8624\n",
      "Epoch: 49/100 Train Loss: 0.8092 Accuracy: 0.8828 Time: 7.37223  | Val Loss: 0.8777 Accuracy: 0.8553\n",
      "Epoch: 50/100 Train Loss: 0.8084 Accuracy: 0.8857 Time: 7.18327  | Val Loss: 0.8692 Accuracy: 0.8624\n",
      "Epoch: 51/100 Train Loss: 0.7962 Accuracy: 0.8883 Time: 6.59871  | Val Loss: 0.8430 Accuracy: 0.8688\n",
      "Epoch: 52/100 Train Loss: 0.7959 Accuracy: 0.8872 Time: 7.30045  | Val Loss: 0.8498 Accuracy: 0.8693\n",
      "Epoch: 53/100 Train Loss: 0.7888 Accuracy: 0.8938 Time: 7.51602  | Val Loss: 0.8619 Accuracy: 0.8655\n",
      "Epoch: 54/100 Train Loss: 0.7798 Accuracy: 0.8969 Time: 6.39307  | Val Loss: 0.8527 Accuracy: 0.8622\n",
      "Epoch: 55/100 Train Loss: 0.7746 Accuracy: 0.9020 Time: 5.77417  | Val Loss: 0.8587 Accuracy: 0.8609\n",
      "Epoch: 56/100 Train Loss: 0.7752 Accuracy: 0.9009 Time: 5.79575  | Val Loss: 0.8678 Accuracy: 0.8589\n",
      "Epoch: 57/100 Train Loss: 0.7667 Accuracy: 0.9052 Time: 5.81695  | Val Loss: 0.8447 Accuracy: 0.8690\n",
      "Epoch: 58/100 Train Loss: 0.7636 Accuracy: 0.9074 Time: 5.74815  | Val Loss: 0.8483 Accuracy: 0.8665\n",
      "Epoch: 59/100 Train Loss: 0.7606 Accuracy: 0.9080 Time: 5.77892  | Val Loss: 0.8519 Accuracy: 0.8652\n",
      "Epoch: 60/100 Train Loss: 0.7483 Accuracy: 0.9119 Time: 5.77118  | Val Loss: 0.8355 Accuracy: 0.8724\n",
      "Epoch: 61/100 Train Loss: 0.7546 Accuracy: 0.9083 Time: 7.08300  | Val Loss: 0.8445 Accuracy: 0.8662\n",
      "Epoch: 62/100 Train Loss: 0.7506 Accuracy: 0.9125 Time: 6.65441  | Val Loss: 0.8425 Accuracy: 0.8701\n",
      "Epoch: 63/100 Train Loss: 0.7403 Accuracy: 0.9157 Time: 6.66568  | Val Loss: 0.8316 Accuracy: 0.8746\n",
      "Epoch: 64/100 Train Loss: 0.7401 Accuracy: 0.9168 Time: 6.49520  | Val Loss: 0.8399 Accuracy: 0.8741\n",
      "Epoch: 65/100 Train Loss: 0.7373 Accuracy: 0.9165 Time: 7.40567  | Val Loss: 0.8348 Accuracy: 0.8708\n",
      "Epoch: 66/100 Train Loss: 0.7286 Accuracy: 0.9215 Time: 7.28301  | Val Loss: 0.8407 Accuracy: 0.8716\n",
      "Epoch: 67/100 Train Loss: 0.7252 Accuracy: 0.9220 Time: 7.00169  | Val Loss: 0.8368 Accuracy: 0.8711\n",
      "Epoch: 68/100 Train Loss: 0.7279 Accuracy: 0.9196 Time: 6.64038  | Val Loss: 0.8349 Accuracy: 0.8729\n",
      "Epoch: 69/100 Train Loss: 0.7163 Accuracy: 0.9281 Time: 6.86232  | Val Loss: 0.8272 Accuracy: 0.8716\n",
      "Epoch: 70/100 Train Loss: 0.7162 Accuracy: 0.9258 Time: 7.69861  | Val Loss: 0.8277 Accuracy: 0.8785\n",
      "Epoch: 71/100 Train Loss: 0.7191 Accuracy: 0.9272 Time: 7.04153  | Val Loss: 0.8276 Accuracy: 0.8708\n",
      "Epoch: 72/100 Train Loss: 0.7069 Accuracy: 0.9308 Time: 6.97047  | Val Loss: 0.8333 Accuracy: 0.8739\n",
      "Epoch: 73/100 Train Loss: 0.7061 Accuracy: 0.9330 Time: 6.45055  | Val Loss: 0.8233 Accuracy: 0.8787\n",
      "Epoch: 74/100 Train Loss: 0.7074 Accuracy: 0.9287 Time: 6.82528  | Val Loss: 0.8248 Accuracy: 0.8769\n",
      "Epoch: 75/100 Train Loss: 0.7034 Accuracy: 0.9339 Time: 7.45700  | Val Loss: 0.8189 Accuracy: 0.8777\n",
      "Epoch: 76/100 Train Loss: 0.6967 Accuracy: 0.9343 Time: 7.11472  | Val Loss: 0.8182 Accuracy: 0.8757\n",
      "Epoch: 77/100 Train Loss: 0.6996 Accuracy: 0.9348 Time: 7.23238  | Val Loss: 0.8209 Accuracy: 0.8744\n",
      "Epoch: 78/100 Train Loss: 0.6972 Accuracy: 0.9355 Time: 6.41025  | Val Loss: 0.8214 Accuracy: 0.8792\n",
      "Epoch: 79/100 Train Loss: 0.7025 Accuracy: 0.9321 Time: 6.76520  | Val Loss: 0.8189 Accuracy: 0.8772\n",
      "Epoch: 80/100 Train Loss: 0.6922 Accuracy: 0.9366 Time: 7.56783  | Val Loss: 0.8215 Accuracy: 0.8767\n",
      "Epoch: 81/100 Train Loss: 0.6885 Accuracy: 0.9396 Time: 7.11614  | Val Loss: 0.8187 Accuracy: 0.8772\n",
      "Epoch: 82/100 Train Loss: 0.6857 Accuracy: 0.9428 Time: 6.93921  | Val Loss: 0.8183 Accuracy: 0.8769\n",
      "Epoch: 83/100 Train Loss: 0.6920 Accuracy: 0.9391 Time: 6.42149  | Val Loss: 0.8264 Accuracy: 0.8734\n",
      "Epoch: 84/100 Train Loss: 0.6875 Accuracy: 0.9386 Time: 6.80341  | Val Loss: 0.8165 Accuracy: 0.8792\n",
      "Epoch: 85/100 Train Loss: 0.6847 Accuracy: 0.9432 Time: 7.38249  | Val Loss: 0.8153 Accuracy: 0.8775\n",
      "Epoch: 86/100 Train Loss: 0.6840 Accuracy: 0.9425 Time: 7.06101  | Val Loss: 0.8187 Accuracy: 0.8772\n",
      "Epoch: 87/100 Train Loss: 0.6815 Accuracy: 0.9430 Time: 6.93149  | Val Loss: 0.8178 Accuracy: 0.8777\n",
      "Epoch: 88/100 Train Loss: 0.6802 Accuracy: 0.9433 Time: 6.37674  | Val Loss: 0.8176 Accuracy: 0.8764\n",
      "Epoch: 89/100 Train Loss: 0.6786 Accuracy: 0.9435 Time: 6.82880  | Val Loss: 0.8189 Accuracy: 0.8777\n",
      "Epoch: 90/100 Train Loss: 0.6772 Accuracy: 0.9460 Time: 7.33037  | Val Loss: 0.8156 Accuracy: 0.8787\n",
      "Epoch: 91/100 Train Loss: 0.6768 Accuracy: 0.9451 Time: 7.35003  | Val Loss: 0.8133 Accuracy: 0.8792\n",
      "Epoch: 92/100 Train Loss: 0.6778 Accuracy: 0.9457 Time: 7.26861  | Val Loss: 0.8154 Accuracy: 0.8805\n",
      "Epoch: 93/100 Train Loss: 0.6726 Accuracy: 0.9461 Time: 6.40310  | Val Loss: 0.8147 Accuracy: 0.8790\n",
      "Epoch: 94/100 Train Loss: 0.6737 Accuracy: 0.9452 Time: 6.76180  | Val Loss: 0.8144 Accuracy: 0.8795\n",
      "Epoch: 95/100 Train Loss: 0.6751 Accuracy: 0.9466 Time: 6.84459  | Val Loss: 0.8114 Accuracy: 0.8808\n",
      "Epoch: 96/100 Train Loss: 0.6708 Accuracy: 0.9485 Time: 7.49071  | Val Loss: 0.8137 Accuracy: 0.8797\n",
      "Epoch: 97/100 Train Loss: 0.6765 Accuracy: 0.9427 Time: 7.10719  | Val Loss: 0.8166 Accuracy: 0.8777\n",
      "Epoch: 98/100 Train Loss: 0.6672 Accuracy: 0.9512 Time: 6.65849  | Val Loss: 0.8138 Accuracy: 0.8790\n",
      "Epoch: 99/100 Train Loss: 0.6760 Accuracy: 0.9464 Time: 6.65083  | Val Loss: 0.8155 Accuracy: 0.8785\n",
      "Epoch: 100/100 Train Loss: 0.6730 Accuracy: 0.9443 Time: 6.76894  | Val Loss: 0.8175 Accuracy: 0.8772\n",
      "#Parameter: 829194 Accuracy: 0.8771974522292993\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_50 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.5,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7248958f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 218378\n",
      "Epoch: 1/100 Train Loss: 2.0854 Accuracy: 0.2563 Time: 5.72556  | Val Loss: 2.0358 Accuracy: 0.2846\n",
      "Epoch: 2/100 Train Loss: 1.9061 Accuracy: 0.3611 Time: 5.97036  | Val Loss: 1.9750 Accuracy: 0.3559\n",
      "Epoch: 3/100 Train Loss: 1.7826 Accuracy: 0.4299 Time: 6.48467  | Val Loss: 1.7037 Accuracy: 0.4698\n",
      "Epoch: 4/100 Train Loss: 1.6980 Accuracy: 0.4725 Time: 6.27422  | Val Loss: 1.5573 Accuracy: 0.5376\n",
      "Epoch: 5/100 Train Loss: 1.6299 Accuracy: 0.5067 Time: 5.80984  | Val Loss: 1.5043 Accuracy: 0.5656\n",
      "Epoch: 6/100 Train Loss: 1.5854 Accuracy: 0.5303 Time: 6.53588  | Val Loss: 1.4381 Accuracy: 0.5954\n",
      "Epoch: 7/100 Train Loss: 1.5458 Accuracy: 0.5488 Time: 6.44825  | Val Loss: 1.5098 Accuracy: 0.5687\n",
      "Epoch: 8/100 Train Loss: 1.5002 Accuracy: 0.5705 Time: 5.77631  | Val Loss: 1.3840 Accuracy: 0.6204\n",
      "Epoch: 9/100 Train Loss: 1.4775 Accuracy: 0.5819 Time: 6.34738  | Val Loss: 1.3446 Accuracy: 0.6464\n",
      "Epoch: 10/100 Train Loss: 1.4491 Accuracy: 0.6007 Time: 6.65502  | Val Loss: 1.3202 Accuracy: 0.6566\n",
      "Epoch: 11/100 Train Loss: 1.4112 Accuracy: 0.6162 Time: 6.22059  | Val Loss: 1.3196 Accuracy: 0.6527\n",
      "Epoch: 12/100 Train Loss: 1.3749 Accuracy: 0.6305 Time: 6.11897  | Val Loss: 1.3142 Accuracy: 0.6558\n",
      "Epoch: 13/100 Train Loss: 1.3569 Accuracy: 0.6370 Time: 6.47652  | Val Loss: 1.2701 Accuracy: 0.6724\n",
      "Epoch: 14/100 Train Loss: 1.3486 Accuracy: 0.6405 Time: 6.26727  | Val Loss: 1.2546 Accuracy: 0.6792\n",
      "Epoch: 15/100 Train Loss: 1.3087 Accuracy: 0.6599 Time: 5.69010  | Val Loss: 1.2207 Accuracy: 0.7060\n",
      "Epoch: 16/100 Train Loss: 1.2989 Accuracy: 0.6658 Time: 5.56246  | Val Loss: 1.1855 Accuracy: 0.7200\n",
      "Epoch: 17/100 Train Loss: 1.2839 Accuracy: 0.6698 Time: 6.94043  | Val Loss: 1.1673 Accuracy: 0.7223\n",
      "Epoch: 18/100 Train Loss: 1.2559 Accuracy: 0.6833 Time: 6.17553  | Val Loss: 1.1755 Accuracy: 0.7187\n",
      "Epoch: 19/100 Train Loss: 1.2418 Accuracy: 0.6928 Time: 5.75416  | Val Loss: 1.1937 Accuracy: 0.7131\n",
      "Epoch: 20/100 Train Loss: 1.2223 Accuracy: 0.6946 Time: 5.86373  | Val Loss: 1.1834 Accuracy: 0.7177\n",
      "Epoch: 21/100 Train Loss: 1.2126 Accuracy: 0.7036 Time: 6.85939  | Val Loss: 1.1257 Accuracy: 0.7439\n",
      "Epoch: 22/100 Train Loss: 1.1930 Accuracy: 0.7094 Time: 6.55568  | Val Loss: 1.1871 Accuracy: 0.7271\n",
      "Epoch: 23/100 Train Loss: 1.1856 Accuracy: 0.7186 Time: 5.87890  | Val Loss: 1.1534 Accuracy: 0.7348\n",
      "Epoch: 24/100 Train Loss: 1.1704 Accuracy: 0.7209 Time: 6.16251  | Val Loss: 1.1128 Accuracy: 0.7496\n",
      "Epoch: 25/100 Train Loss: 1.1554 Accuracy: 0.7308 Time: 6.75611  | Val Loss: 1.0863 Accuracy: 0.7582\n",
      "Epoch: 26/100 Train Loss: 1.1445 Accuracy: 0.7362 Time: 6.31811  | Val Loss: 1.0729 Accuracy: 0.7694\n",
      "Epoch: 27/100 Train Loss: 1.1346 Accuracy: 0.7370 Time: 5.74765  | Val Loss: 1.0763 Accuracy: 0.7659\n",
      "Epoch: 28/100 Train Loss: 1.1111 Accuracy: 0.7506 Time: 6.96117  | Val Loss: 1.0595 Accuracy: 0.7781\n",
      "Epoch: 29/100 Train Loss: 1.1252 Accuracy: 0.7436 Time: 6.16843  | Val Loss: 1.0738 Accuracy: 0.7676\n",
      "Epoch: 30/100 Train Loss: 1.1037 Accuracy: 0.7507 Time: 5.84879  | Val Loss: 1.0462 Accuracy: 0.7819\n",
      "Epoch: 31/100 Train Loss: 1.0994 Accuracy: 0.7517 Time: 5.70145  | Val Loss: 1.0298 Accuracy: 0.7837\n",
      "Epoch: 32/100 Train Loss: 1.0924 Accuracy: 0.7560 Time: 6.60120  | Val Loss: 1.0475 Accuracy: 0.7827\n",
      "Epoch: 33/100 Train Loss: 1.0817 Accuracy: 0.7642 Time: 6.18252  | Val Loss: 1.0244 Accuracy: 0.7913\n",
      "Epoch: 34/100 Train Loss: 1.0661 Accuracy: 0.7704 Time: 5.99386  | Val Loss: 1.0404 Accuracy: 0.7852\n",
      "Epoch: 35/100 Train Loss: 1.0561 Accuracy: 0.7743 Time: 6.34543  | Val Loss: 1.0071 Accuracy: 0.7931\n",
      "Epoch: 36/100 Train Loss: 1.0492 Accuracy: 0.7797 Time: 6.64453  | Val Loss: 1.0543 Accuracy: 0.7827\n",
      "Epoch: 37/100 Train Loss: 1.0462 Accuracy: 0.7754 Time: 6.21063  | Val Loss: 0.9935 Accuracy: 0.8018\n",
      "Epoch: 38/100 Train Loss: 1.0361 Accuracy: 0.7834 Time: 5.97452  | Val Loss: 0.9826 Accuracy: 0.8092\n",
      "Epoch: 39/100 Train Loss: 1.0308 Accuracy: 0.7873 Time: 6.58036  | Val Loss: 0.9852 Accuracy: 0.8099\n",
      "Epoch: 40/100 Train Loss: 1.0285 Accuracy: 0.7854 Time: 6.29890  | Val Loss: 1.0000 Accuracy: 0.8036\n",
      "Epoch: 41/100 Train Loss: 1.0269 Accuracy: 0.7906 Time: 5.82456  | Val Loss: 0.9803 Accuracy: 0.8092\n",
      "Epoch: 42/100 Train Loss: 1.0045 Accuracy: 0.8012 Time: 5.93857  | Val Loss: 1.0011 Accuracy: 0.8018\n",
      "Epoch: 43/100 Train Loss: 1.0073 Accuracy: 0.7954 Time: 6.77797  | Val Loss: 0.9802 Accuracy: 0.8115\n",
      "Epoch: 44/100 Train Loss: 0.9970 Accuracy: 0.7997 Time: 6.41011  | Val Loss: 0.9923 Accuracy: 0.8041\n",
      "Epoch: 45/100 Train Loss: 0.9946 Accuracy: 0.8016 Time: 5.93825  | Val Loss: 0.9620 Accuracy: 0.8168\n",
      "Epoch: 46/100 Train Loss: 0.9946 Accuracy: 0.7966 Time: 6.34194  | Val Loss: 0.9519 Accuracy: 0.8222\n",
      "Epoch: 47/100 Train Loss: 0.9723 Accuracy: 0.8157 Time: 6.31880  | Val Loss: 0.9721 Accuracy: 0.8120\n",
      "Epoch: 48/100 Train Loss: 0.9775 Accuracy: 0.8099 Time: 5.96153  | Val Loss: 0.9789 Accuracy: 0.8120\n",
      "Epoch: 49/100 Train Loss: 0.9616 Accuracy: 0.8176 Time: 5.94357  | Val Loss: 0.9630 Accuracy: 0.8237\n",
      "Epoch: 50/100 Train Loss: 0.9662 Accuracy: 0.8110 Time: 7.13176  | Val Loss: 0.9649 Accuracy: 0.8194\n",
      "Epoch: 51/100 Train Loss: 0.9569 Accuracy: 0.8195 Time: 6.43255  | Val Loss: 0.9508 Accuracy: 0.8296\n",
      "Epoch: 52/100 Train Loss: 0.9515 Accuracy: 0.8224 Time: 5.95623  | Val Loss: 0.9480 Accuracy: 0.8260\n",
      "Epoch: 53/100 Train Loss: 0.9416 Accuracy: 0.8311 Time: 5.99872  | Val Loss: 0.9732 Accuracy: 0.8132\n",
      "Epoch: 54/100 Train Loss: 0.9469 Accuracy: 0.8245 Time: 7.01544  | Val Loss: 0.9562 Accuracy: 0.8257\n",
      "Epoch: 55/100 Train Loss: 0.9371 Accuracy: 0.8248 Time: 6.54617  | Val Loss: 0.9472 Accuracy: 0.8285\n",
      "Epoch: 56/100 Train Loss: 0.9371 Accuracy: 0.8255 Time: 6.18598  | Val Loss: 0.9494 Accuracy: 0.8260\n",
      "Epoch: 57/100 Train Loss: 0.9342 Accuracy: 0.8287 Time: 6.65743  | Val Loss: 0.9318 Accuracy: 0.8341\n",
      "Epoch: 58/100 Train Loss: 0.9304 Accuracy: 0.8298 Time: 6.52615  | Val Loss: 0.9388 Accuracy: 0.8316\n",
      "Epoch: 59/100 Train Loss: 0.9260 Accuracy: 0.8310 Time: 5.79705  | Val Loss: 0.9586 Accuracy: 0.8222\n",
      "Epoch: 60/100 Train Loss: 0.9212 Accuracy: 0.8365 Time: 6.10877  | Val Loss: 0.9332 Accuracy: 0.8252\n",
      "Epoch: 61/100 Train Loss: 0.9084 Accuracy: 0.8384 Time: 6.91581  | Val Loss: 0.9400 Accuracy: 0.8288\n",
      "Epoch: 62/100 Train Loss: 0.9011 Accuracy: 0.8478 Time: 6.51359  | Val Loss: 0.9376 Accuracy: 0.8288\n",
      "Epoch: 63/100 Train Loss: 0.9031 Accuracy: 0.8407 Time: 6.02433  | Val Loss: 0.9305 Accuracy: 0.8369\n",
      "Epoch: 64/100 Train Loss: 0.9034 Accuracy: 0.8450 Time: 6.72171  | Val Loss: 0.9402 Accuracy: 0.8257\n",
      "Epoch: 65/100 Train Loss: 0.8981 Accuracy: 0.8478 Time: 6.39667  | Val Loss: 0.9296 Accuracy: 0.8273\n",
      "Epoch: 66/100 Train Loss: 0.8854 Accuracy: 0.8478 Time: 5.88110  | Val Loss: 0.9311 Accuracy: 0.8364\n",
      "Epoch: 67/100 Train Loss: 0.8860 Accuracy: 0.8523 Time: 6.17149  | Val Loss: 0.9270 Accuracy: 0.8364\n",
      "Epoch: 68/100 Train Loss: 0.8891 Accuracy: 0.8538 Time: 7.03765  | Val Loss: 0.9258 Accuracy: 0.8336\n",
      "Epoch: 69/100 Train Loss: 0.8801 Accuracy: 0.8557 Time: 6.55452  | Val Loss: 0.9249 Accuracy: 0.8400\n",
      "Epoch: 70/100 Train Loss: 0.8729 Accuracy: 0.8591 Time: 5.64796  | Val Loss: 0.9275 Accuracy: 0.8359\n",
      "Epoch: 71/100 Train Loss: 0.8706 Accuracy: 0.8603 Time: 5.69802  | Val Loss: 0.9181 Accuracy: 0.8354\n",
      "Epoch: 72/100 Train Loss: 0.8773 Accuracy: 0.8568 Time: 6.70834  | Val Loss: 0.9154 Accuracy: 0.8387\n",
      "Epoch: 73/100 Train Loss: 0.8695 Accuracy: 0.8611 Time: 6.20819  | Val Loss: 0.9172 Accuracy: 0.8357\n",
      "Epoch: 74/100 Train Loss: 0.8694 Accuracy: 0.8573 Time: 5.68621  | Val Loss: 0.9162 Accuracy: 0.8377\n",
      "Epoch: 75/100 Train Loss: 0.8683 Accuracy: 0.8588 Time: 5.96505  | Val Loss: 0.9237 Accuracy: 0.8344\n",
      "Epoch: 76/100 Train Loss: 0.8620 Accuracy: 0.8636 Time: 6.74962  | Val Loss: 0.9237 Accuracy: 0.8395\n",
      "Epoch: 77/100 Train Loss: 0.8581 Accuracy: 0.8625 Time: 6.62104  | Val Loss: 0.9151 Accuracy: 0.8408\n",
      "Epoch: 78/100 Train Loss: 0.8586 Accuracy: 0.8638 Time: 6.28271  | Val Loss: 0.9178 Accuracy: 0.8362\n",
      "Epoch: 79/100 Train Loss: 0.8567 Accuracy: 0.8661 Time: 6.54518  | Val Loss: 0.9130 Accuracy: 0.8420\n",
      "Epoch: 80/100 Train Loss: 0.8548 Accuracy: 0.8621 Time: 6.35320  | Val Loss: 0.9164 Accuracy: 0.8438\n",
      "Epoch: 81/100 Train Loss: 0.8445 Accuracy: 0.8708 Time: 6.13638  | Val Loss: 0.9140 Accuracy: 0.8372\n",
      "Epoch: 82/100 Train Loss: 0.8516 Accuracy: 0.8658 Time: 6.11219  | Val Loss: 0.9156 Accuracy: 0.8395\n",
      "Epoch: 83/100 Train Loss: 0.8524 Accuracy: 0.8671 Time: 6.85355  | Val Loss: 0.9176 Accuracy: 0.8390\n",
      "Epoch: 84/100 Train Loss: 0.8459 Accuracy: 0.8705 Time: 6.46836  | Val Loss: 0.9168 Accuracy: 0.8387\n",
      "Epoch: 85/100 Train Loss: 0.8493 Accuracy: 0.8698 Time: 5.79459  | Val Loss: 0.9172 Accuracy: 0.8405\n",
      "Epoch: 86/100 Train Loss: 0.8404 Accuracy: 0.8720 Time: 6.21475  | Val Loss: 0.9111 Accuracy: 0.8413\n",
      "Epoch: 87/100 Train Loss: 0.8405 Accuracy: 0.8707 Time: 7.09303  | Val Loss: 0.9119 Accuracy: 0.8425\n",
      "Epoch: 88/100 Train Loss: 0.8360 Accuracy: 0.8731 Time: 6.25522  | Val Loss: 0.9134 Accuracy: 0.8428\n",
      "Epoch: 89/100 Train Loss: 0.8342 Accuracy: 0.8761 Time: 6.04755  | Val Loss: 0.9140 Accuracy: 0.8425\n",
      "Epoch: 90/100 Train Loss: 0.8390 Accuracy: 0.8740 Time: 5.25356  | Val Loss: 0.9142 Accuracy: 0.8385\n",
      "Epoch: 91/100 Train Loss: 0.8366 Accuracy: 0.8732 Time: 5.25417  | Val Loss: 0.9120 Accuracy: 0.8433\n",
      "Epoch: 92/100 Train Loss: 0.8396 Accuracy: 0.8707 Time: 5.36274  | Val Loss: 0.9092 Accuracy: 0.8423\n",
      "Epoch: 93/100 Train Loss: 0.8411 Accuracy: 0.8711 Time: 5.35769  | Val Loss: 0.9130 Accuracy: 0.8403\n",
      "Epoch: 94/100 Train Loss: 0.8257 Accuracy: 0.8811 Time: 5.67559  | Val Loss: 0.9114 Accuracy: 0.8420\n",
      "Epoch: 95/100 Train Loss: 0.8333 Accuracy: 0.8752 Time: 4.94314  | Val Loss: 0.9097 Accuracy: 0.8403\n",
      "Epoch: 96/100 Train Loss: 0.8389 Accuracy: 0.8733 Time: 4.99767  | Val Loss: 0.9066 Accuracy: 0.8428\n",
      "Epoch: 97/100 Train Loss: 0.8367 Accuracy: 0.8773 Time: 7.13741  | Val Loss: 0.9087 Accuracy: 0.8428\n",
      "Epoch: 98/100 Train Loss: 0.8298 Accuracy: 0.8783 Time: 6.51961  | Val Loss: 0.9079 Accuracy: 0.8415\n",
      "Epoch: 99/100 Train Loss: 0.8347 Accuracy: 0.8749 Time: 6.15930  | Val Loss: 0.9084 Accuracy: 0.8428\n",
      "Epoch: 100/100 Train Loss: 0.8351 Accuracy: 0.8734 Time: 6.39049  | Val Loss: 0.9096 Accuracy: 0.8441\n",
      "#Parameter: 218378 Accuracy: 0.8438216560509554\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_25 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.25,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6c2d68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = width_multiplier_1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(width_multiplier_1[field], label=\"Width-1\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_75[field], label=\"Width-0.75\")\n",
    "    plt.plot(width_multiplier_50[field], label=\"Width-0.5\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_25[field], label=\"Width-0.25\", linestyle=\"-\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5a03c64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
